Independent system operators are responsible for maintaining an instantaneous and continuous balance between supply and demand of power system through managing the energy and reserve transactions of energy and reserve markets, including day-ahead market and real-time market. According to the forecasted or historical load and non-dispatchable generation profiles for next day, the commitment schedule of dispatchable generation units for next 24 hours are determined through solving a security-constrained unit commitment problem and a security-constrained economic dispatch problem. The unit commitment is determined by finding the least cost unit commitment solution for the second day while respecting both system-wide and unit-wise constraints. By fixing the unit commitment variables, the economic dispatch is solved, and the locational marginal price for energy and reserve are then obtained as byproducts of the economic dispatch problem. This task is complicated by the increased presence of distributed energy resources and the continuing improvements on market regulations.
The unpredictable nature of renewable energy sources leads to greater fluctuations in the amount of generated power available. To achieve a power balance in the presence of heightened volatility, the operators have to increase the reserve capacities and chose more fast-response units to reduce the power outage risk at certain cost. The goal of co-optimization of energy and reserve is to make a best compromise between uncertainty and cost. The challenge is that the renewables may demonstrate different characteristics in term of fluctuation magnitudes and frequency if the renewable data sets are collected using different sampling rates. A unit commitment schedule, that conventionally determined based on renewable profiles generated at longer time scale (such as one sample each hour) might not be optimal when implemented in real time. On one hand, it might not have sufficient reserves to deal with actual renewable variation measured at shorter time scale (such as one sample each 5 minutes, or 4 seconds). Or, the response speeds of chosen generating units might not fast enough to catch up the renewable variation frequency measured in such shorter scale. On the other hand, the unit commitment schedule might have chosen too much reserve or too many fast-response generation units that caused losing the economic efficiency.
Meanwhile, the market regulatory rules have also required the generation units rewarded by their services that they have actually provided or achieved in real time. For example, the generate units acting for frequency regulation resources should be compensated based on their actual contributions to the system frequency quality. That is, the payment should reflect the quality of frequency regulation service provided by the resource when the resource is required to follow system regulation signal. The signal following is typically implemented through a frequency controller of the generating unit. This requirement is usually satisfied through a two-step procedure. The generation units are initially rewarded by the prices determined by the day-ahead market based on the hourly profiles. The payments are then adjusted after the operation cycle based on real-time prices determined according to actual real-time profiles. Such approach can be easily implemented, but the generation plants hardly get their benefits maximum through such procedure. The main reason is that when the commitment status of a generation unit is determined, its contribution and performance for frequency regulation are highly constrained by its committed status, since the generating unit has to keep on its commitment statues for a while due to its technical minimum up/down time constraints. Without taking the real-time renewable and load fluctuations into account in some manners, the gaps or deviations between day-ahead schedules and real-time dispatch and control hardly be mitigated.
There are several methods existing to deal with the unit commitment problem with stochastic characteristics. For example, U.S. Pat. No. 7,349,882 B2 disclosed a method for optimizing security constrained unit commitment in the day ahead wholesale electricity market using mixed integer linear programming techniques. U.S. Pat. No. 9,031,798 B2 disclosed systems and methods for solving large-scale stochastic unit commitment problems using a branch-cut-price algorithm. However, those existing approaches have not considered the actual generation dispatch and frequency regulation performance for generation units with sufficient details in the determination of generation commitment schedule.
Therefore, there is a need for developing new approaches for the day-ahead power market to appropriately considering the real-time operation and control and resource and load uncertainty when co-optimizing the energy production and frequency regulation.